AI frameworks and agent build
Agents that own an outcome within boundaries you set
An assistant does what it is told. An agent plans, uses tools, iterates until the goal is met, and hands over when it should not decide alone. We build agents and multi-agent systems on a proven framework, with the governance, evaluation and escalation that make them safe to run.
From assisting people to owning outcomes
The four levels of AI run from a copilot that suggests, through an assistant that executes on instruction, to an agent that plans and executes within boundaries, and an agentic system in which specialised agents share state and are orchestrated under policy controls. The strategic shift is from AI that assists humans to AI that owns outcomes within governed boundaries, and it is where the value is in workflows that are measurable, repetitive and bounded.
Which workflows those are is a discovery question. The build question is how to make the agent trustworthy: what it may do, what it may touch, how its decisions are checked, and when it must stop and ask.
What every agent we build has
A planning loop with tools
The agent breaks the goal into steps, calls the systems it needs, checks results and iterates. Tools are explicit, typed and permissioned; the agent cannot reach what it has not been given.
Policy controls
What the agent may decide alone, what needs approval, and what it must never do, written as rules it is checked against, not as hopes in a prompt.
Confidence scoring and escalation
Every decision scored; below the threshold the case goes to a person with the agent's reasoning attached. Thresholds differ by action and by the cost of getting it wrong.
Evaluation harness
A set of scenarios with agreed outcomes, run on every change. Accuracy, escalation rate, cost and time per task reported weekly.
Shared state and audit
For multi-agent systems, a shared, auditable record of what each agent knew and did, with hierarchical delegation and clear escalation paths between them.
Monitoring for drift
Agents operate on changing data and changing models; we monitor for performance drift and bias and re-run the evaluation set whenever either changes.
Workflows we build agents for
Support and ticket triage
Reads code and documentation, drafts the reply, escalates on low confidence. In production at Agency51.
Supplier and market intelligence
Agents that monitor sources, extract commodity, competitor and supplier signals, and compile battle cards and risk alerts with sources cited.
Quote and configuration
Plans the quote workflow, resolves pricing and compliance rules, and delivers an approved quote, with a person approving above a value threshold.
Renewal and contact
Identifies warranty-end and legacy-product customers from the systems of record, drafts the contact and routes it to the account owner.
Back-office workflow automation
Purchase-order raising, report compilation, inbox triage, meeting minutes to actions: the routine work that every function described the same way in discovery.
Built with the tools we use ourselves
We build on Claude, OpenAI and open-weight models and choose on the evidence for each use case; the framework is model-agnostic so a change of model is a configuration change. Tool access goes through typed interfaces and, where the systems support it, the Model Context Protocol. Our own delivery runs on Claude Code, and the same engineering standards, review gates and evaluation discipline apply to what we build for clients as to what we build for ourselves.
One bounded workflow first
Agents are built one bounded workflow at a time: two weeks of scoping (the goal, the tools, the policies, the escalation rules and the evaluation scenarios), three to eight weeks of build, a production pilot with a person reviewing every escalation, then handover or a managed service. Multi-agent systems are scoped as a programme after the first agent has proved itself.
About agent development
How is an agent different from automation we already have?
Conventional automation follows a fixed path and fails when the input varies. An agent reasons about the input, chooses the steps, handles variation, and knows when to stop and ask. It is for work that was too variable to automate before.
Can an agent act in our ERP or CRM?
Yes, through explicit, permissioned tools. What it may read and write is defined in scope; actions above an agreed threshold of value or risk require approval.
What happens when it is wrong?
The escalation path catches most cases before they act; the audit trail shows what happened for the rest; and every error becomes an evaluation scenario so it cannot recur silently. We also tell you what went wrong, in writing, every time.
Do you build agentic systems from day one?
No. One agent, one workflow, proved in production. Agentic systems come when there are several proven agents whose outcomes need coordinating.
Pick the workflow that wastes the most time
That is usually the right first agent. Tell us what it looks like today.